Sökning: onr:"swepub:oai:DiVA.org:kth-326918" >
Predicting the wall...
Predicting the wall-shear stress and wall pressure through convolutional neural networks
-
- Geetha Balasubramanian, Arivazhagan (författare)
- KTH,Teknisk mekanik
-
- Guastoni, Luca (författare)
- KTH,SeRC - Swedish e-Science Research Centre,Turbulent simulations laboratory
-
- Schlatter, Philipp (författare)
- KTH,Linné Flow Center, FLOW,SeRC - Swedish e-Science Research Centre,Strömningsmekanik och Teknisk Akustik,Lehrstuhls für Strömungsmechanik (LSTM), Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Germany
-
visa fler...
-
- Azizpour, Hossein, 1985- (författare)
- KTH,Robotik, perception och lärande, RPL,SeRC - Swedish e-Science Research Centre
-
- Vinuesa, Ricardo (författare)
- KTH,Linné Flow Center, FLOW,SeRC - Swedish e-Science Research Centre,Strömningsmekanik och Teknisk Akustik
-
visa färre...
-
(creator_code:org_t)
- Engelska.
- Relaterad länk:
-
https://kth.diva-por... (primary) (Raw object)
-
visa fler...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to predict the 2D velocity-fluctuation fields at the inner-scaled wall-normal location y+ target, using the sampled velocity fluctuations in wall-parallel planes located farther from the wall, at y+ input. The predictions from the FCN are compared against the predictions from a proposed R-Net architecture. Since the R-Net model is found to perform better than the FCN model, the former architecture is optimized to predict the 2D streamwise and spanwise wall-shear-stress components and the wall pressure from the sampled velocity-fluctuation fields farther from the wall. The dataset is obtained from DNS of open channel flow at Reτ=180 and 550. The turbulent velocity-fluctuation fields are sampled at various inner-scaled wall-normal locations, along with the wall-shear stress and the wall pressure. At Reτ=550, both FCN and R-Net can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at y+=50 using the velocity-fluctuation fields at y+=100 as input with about 10% error in prediction of streamwise-fluctuations intensity. Further, the R-Net is also able to predict the wall-shear-stress and wall-pressure fields using the velocity-fluctuation fields at y+=50 with around 10% error in the intensity of the corresponding fluctuations at both Reτ=180 and 550. These results are an encouraging starting point to develop neural-network-based approaches for modelling turbulence near the wall in large-eddy simulations.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
Nyckelord
- Turbulent channel flow
- wall-shear stress
- deep learning
- fully-convolutional network
- self-similarity
Publikations- och innehållstyp
- vet (ämneskategori)
- ovr (ämneskategori)